Iterative Accurate Seismic Data Deblending by ASB-Based Robust Sparse Radon Transform

Abstract

Blended acquisition can improve the acquisition efficiency, and thereby reduce the acquisition costs. It becomes an important acquisition method in seismic exploration. However, the blending noise imposes challenges for subsequent traditional seismic data processing procedures, and thus deblending algorithms are necessary to obtain deblended seismic data. Based on the assumption that the signal is coherent and the blending noise is randomized in a specific domain, traditional deblending methods using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> -norm measuring the data misfit can obtain separated gathers. However, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> -norm is improper when the appearing seismic noise bias the normal distribution, that is, abnormal noise appears. To attenuate the abnormal noise effects during iterative deblending, we proposed an accurate deblending algorithm based on a robust sparse Radon transform (RSRT). For the RSRT, the alternating split Bregman (ASB) algorithm is used for robust 2-D Radon model updating with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> -norm to measure data misfit in the mixed time–frequency domain and the sparsity constraint to the time-domain Radon model. Using the RSRT iteratively, the Radon model and the corresponding deblended data can be estimated robustly, accurately, and efficiently. Blended synthetic data with different levels of abnormal noise and with different trace intervals demonstrate the validity and flexibility of the proposed robust deblending method quantitatively with a high recovered SNR. Numerically blended field data further prove the effectiveness of the proposed method in attenuating the abnormal and blending noise.